updated

app.py

@@ -1,19 +1,25 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
 import numpy as np
 
-# Load your model (using a relative path)
+# Load your model
 model = load_model('cats_and_dogs_classifier.h5')
 
+# Define prediction function
 def predict(image):
-    img = image.resize((224, 224))
+    # Convert Gradio PIL Image to numpy array
+    img = np.array(image)
+    # Resize and preprocess image
+    img = np.array(Image.fromarray(img).resize((224, 224)))
     img = img_to_array(img)
     img = np.expand_dims(img, axis=0)
+    # Predict class (0 for cat, 1 for dog)
     prediction = model.predict(img)
     class_label = 'cat' if prediction[0][0] < 0.5 else 'dog'
     return class_label
 
 # Create a Gradio interface
-interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="text")
+interface = gr.Interface(fn=predict, inputs="image", outputs="text")
 interface.launch()